Overview

Dataset statistics

Number of variables8
Number of observations280
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 KiB
Average record size in memory68.5 B

Variable types

Categorical5
Numeric3

Dataset

Description경상남도 거창군 지방세 세목별 과세현황에 대한 데이터로 2017년도, 2018년도, 2019년도. 2020년도, 2021년도, 2022년도 세목명, 세원유형명, 부과건수, 부과금액 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15079153/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세원 유형명 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 has 72 (25.7%) zerosZeros
부과금액 has 72 (25.7%) zerosZeros

Reproduction

Analysis started2023-12-12 13:13:26.779857
Analysis finished2023-12-12 13:13:28.178213
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
경상남도
280 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도
2nd row경상남도
3rd row경상남도
4th row경상남도
5th row경상남도

Common Values

ValueCountFrequency (%)
경상남도 280
100.0%

Length

2023-12-12T22:13:28.276821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:13:28.393205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 280
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
거창군
280 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row거창군
2nd row거창군
3rd row거창군
4th row거창군
5th row거창군

Common Values

ValueCountFrequency (%)
거창군 280
100.0%

Length

2023-12-12T22:13:28.504269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:13:28.602938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 280
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
48880
280 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 280
100.0%

Length

2023-12-12T22:13:28.725226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:13:28.828439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 280
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4857
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T22:13:28.941297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7080275
Coefficient of variation (CV)0.0008457735
Kurtosis-1.2643122
Mean2019.4857
Median Absolute Deviation (MAD)1
Skewness0.011507648
Sum565456
Variance2.9173579
MonotonicityIncreasing
2023-12-12T22:13:29.076548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 47
16.8%
2018 47
16.8%
2019 47
16.8%
2020 47
16.8%
2021 46
16.4%
2022 46
16.4%
ValueCountFrequency (%)
2017 47
16.8%
2018 47
16.8%
2019 47
16.8%
2020 47
16.8%
2021 46
16.4%
2022 46
16.4%
ValueCountFrequency (%)
2022 46
16.4%
2021 46
16.4%
2020 47
16.8%
2019 47
16.8%
2018 47
16.8%
2017 47
16.8%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
레저세
24 
Other values (8)
80 

Length

Max length7
Median length3
Mean length3.7142857
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row취득세
5th row취득세

Common Values

ValueCountFrequency (%)
취득세 54
19.3%
주민세 50
17.9%
자동차세 42
15.0%
재산세 30
10.7%
레저세 24
8.6%
지방소득세 24
8.6%
지역자원시설세 14
 
5.0%
등록면허세 12
 
4.3%
담배소비세 6
 
2.1%
교육세 6
 
2.1%
Other values (3) 18
 
6.4%

Length

2023-12-12T22:13:29.233437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.3%
주민세 50
17.9%
자동차세 42
15.0%
재산세 30
10.7%
레저세 24
8.6%
지방소득세 24
8.6%
지역자원시설세 14
 
5.0%
등록면허세 12
 
4.3%
담배소비세 6
 
2.1%
교육세 6
 
2.1%
Other values (3) 18
 
6.4%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
담배소비세
 
6
승합
 
6
주택(단독)
 
6
3륜이하
 
6
기계장비
 
6
Other values (45)
250 

Length

Max length11
Median length8
Mean length6.0357143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row건축물
5th row주택(개별)

Common Values

ValueCountFrequency (%)
담배소비세 6
 
2.1%
승합 6
 
2.1%
주택(단독) 6
 
2.1%
3륜이하 6
 
2.1%
기계장비 6
 
2.1%
차량 6
 
2.1%
선박 6
 
2.1%
토지 6
 
2.1%
재산세(건축물) 6
 
2.1%
주택(개별) 6
 
2.1%
Other values (40) 220
78.6%

Length

2023-12-12T22:13:29.381299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배소비세 6
 
2.1%
항공기 6
 
2.1%
주민세(종합소득 6
 
2.1%
승합 6
 
2.1%
교육세 6
 
2.1%
기타승용 6
 
2.1%
승용 6
 
2.1%
주민세(종업원분 6
 
2.1%
주민세(특별징수 6
 
2.1%
체납 6
 
2.1%
Other values (40) 220
78.6%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8915.6214
Minimum0
Maximum157460
Zeros72
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T22:13:29.528675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median458.5
Q36941.75
95-th percentile33763.45
Maximum157460
Range157460
Interquartile range (IQR)6941.75

Descriptive statistics

Standard deviation24014.422
Coefficient of variation (CV)2.6935219
Kurtosis25.459996
Mean8915.6214
Median Absolute Deviation (MAD)458.5
Skewness4.8042556
Sum2496374
Variance5.7669245 × 108
MonotonicityNot monotonic
2023-12-12T22:13:30.012996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72
 
25.7%
12 6
 
2.1%
11 3
 
1.1%
9 3
 
1.1%
7 2
 
0.7%
410 2
 
0.7%
2 2
 
0.7%
199 2
 
0.7%
25247 2
 
0.7%
445 2
 
0.7%
Other values (184) 184
65.7%
ValueCountFrequency (%)
0 72
25.7%
2 2
 
0.7%
3 1
 
0.4%
4 1
 
0.4%
5 1
 
0.4%
6 1
 
0.4%
7 2
 
0.7%
8 1
 
0.4%
9 3
 
1.1%
11 3
 
1.1%
ValueCountFrequency (%)
157460 1
0.4%
157301 1
0.4%
153635 1
0.4%
149320 1
0.4%
148885 1
0.4%
147832 1
0.4%
57506 1
0.4%
56491 1
0.4%
55191 1
0.4%
54539 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2499185 × 109
Minimum0
Maximum1.0106969 × 1010
Zeros72
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T22:13:30.185217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.658665 × 108
Q31.7284485 × 109
95-th percentile5.3766474 × 109
Maximum1.0106969 × 1010
Range1.0106969 × 1010
Interquartile range (IQR)1.7284485 × 109

Descriptive statistics

Standard deviation1.8507381 × 109
Coefficient of variation (CV)1.480687
Kurtosis3.1014513
Mean1.2499185 × 109
Median Absolute Deviation (MAD)2.658665 × 108
Skewness1.8202775
Sum3.4997718 × 1011
Variance3.4252314 × 1018
MonotonicityNot monotonic
2023-12-12T22:13:30.383945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72
 
25.7%
4430121000 1
 
0.4%
643243000 1
 
0.4%
2870165000 1
 
0.4%
1744161000 1
 
0.4%
1172981000 1
 
0.4%
1388246000 1
 
0.4%
1678972000 1
 
0.4%
4121075000 1
 
0.4%
5493902000 1
 
0.4%
Other values (199) 199
71.1%
ValueCountFrequency (%)
0 72
25.7%
32000 1
 
0.4%
75000 1
 
0.4%
124000 1
 
0.4%
149000 1
 
0.4%
242000 1
 
0.4%
253000 1
 
0.4%
381000 1
 
0.4%
522000 1
 
0.4%
600000 1
 
0.4%
ValueCountFrequency (%)
10106969000 1
0.4%
7966664000 1
0.4%
7921451000 1
0.4%
7706954000 1
0.4%
6608961000 1
0.4%
5979308000 1
0.4%
5856779000 1
0.4%
5767097000 1
0.4%
5749982000 1
0.4%
5555217000 1
0.4%

Interactions

2023-12-12T22:13:27.571763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.021850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.281200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.672483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.100172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.377908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.778486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.191692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:27.467748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:13:30.499576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8160.693
세원 유형명0.0001.0001.0001.0000.903
부과건수0.0000.8161.0001.0000.658
부과금액0.0000.6930.9030.6581.000
2023-12-12T22:13:30.634111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세원 유형명세목명
세원 유형명1.0000.928
세목명0.9281.000
2023-12-12T22:13:30.738490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.0000.0270.0630.0000.000
부과건수0.0271.0000.7580.6130.886
부과금액0.0630.7581.0000.3840.558
세목명0.0000.6130.3841.0000.928
세원 유형명0.0000.8860.5580.9281.000

Missing values

2023-12-12T22:13:27.900469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:13:28.073694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
0경상남도거창군488802017담배소비세담배소비세1094430121000
1경상남도거창군488802017교육세교육세1478325125702000
2경상남도거창군488802017도시계획세도시계획세00
3경상남도거창군488802017취득세건축물6041862828000
4경상남도거창군488802017취득세주택(개별)11081650051000
5경상남도거창군488802017취득세주택(단독)538934756000
6경상남도거창군488802017취득세기타25120084000
7경상남도거창군488802017취득세항공기00
8경상남도거창군488802017취득세기계장비458555535000
9경상남도거창군488802017취득세차량47373661189000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
270경상남도거창군488802022등록면허세등록면허세(면허)11187170642000
271경상남도거창군488802022등록면허세등록면허세(등록)129861126431000
272경상남도거창군488802022지역자원시설세지역자원시설세(소방)20619958296000
273경상남도거창군488802022지역자원시설세지역자원시설세(시설)00
274경상남도거창군488802022지역자원시설세지역자원시설세(특자)20633445000
275경상남도거창군488802022지방소득세지방소득세(특별징수)84223308768000
276경상남도거창군488802022지방소득세지방소득세(법인소득)10282190757000
277경상남도거창군488802022지방소득세지방소득세(양도소득)10991463147000
278경상남도거창군488802022지방소득세지방소득세(종합소득)91061377965000
279경상남도거창군488802022체납체납258061723211000